Online condition diagnosis for a two-stage gearbox machinery of an aerospace utilization system using an ensemble multi-fault features indexing approach

China manned space station is designed to operate for over ten years. Long-term and sustainable research on space science and technology will be conducted during its operation. The application payloads must meet the ‘‘long life and high reliability" mission requirement. Gearbox machinery is one of the essential devices in an aerospace utilization system, failure of which may lead to downtime loss even during some disastrous catastrophes. A fault diagnosis of gearbox has attracted attentions for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. A novel fault diagnosis method based on the Ensemble Multi-Fault Features Indexing(EMFFI) approach is proposed for the condition monitoring of gearboxes. Different from traditional methods of signal analysis in the one-dimensional space, this study employs a supervised learning method to determine the faults of a gearbox in a two-dimensional space using the classification model established by training the features extracted automatically from diagnostic vibration signals captured. The proposed method mainly includes the following steps. First, the vibration signals are transformed into a bi-spectrum contour map utilizing bi-spectrum technology,which provides a basis for the following image-based feature extraction. Then, Speeded-Up Robustness Feature(SURF) is applied to automatically extract the image feature points of the bi-spectrum contour map using a multi-fault features indexing theory, and the feature dimension is reduced by Linear Discriminant Analysis(LDA). Finally, Random Forest(RF) is introduced to identify the fault types of the gearbox. The test results verify that the proposed method based on the multi-fault features indexing approach achieves the target of high diagnostic accuracy and can serve as a highly effective technique to discover faults in a gearbox machinery such as a two-stage one.     

Robust adaptive compensation control for unmanned autonomous helicopter with input saturation and actuator faults

This paper studies a robust adaptive compensation Fault Tolerant Control(FTC) for the medium-scale Unmanned Autonomous Helicopter(UAH) in the presence of external disturbances,actuator faults and input saturation.To improve the disturbance rejection capacity of the UAH system in actuator healthy case, an adaptive control method is adopted to cope with the external disturbances and a nominal controller is proposed to stabilize the system.Meanwhile, compensation control inputs are designed to reduce the negative effects derived from actuator faults and input saturation.Based on the backstepping control and inner-outer loop control technologies, a robust adaptive FTC scheme is developed to guarantee the tracking errors convergence.Under the presented FTC controller, the uniform ultimate boundedness of all closed-loop signals is ensured via Lyapunov stability analysis.Simulation results demonstrate the effectiveness of the proposed control algorithm.     

Aircraft engine fault detection based on grouped convolutional denoising autoencoders

Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System (ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low.     

在轨服务中的仿生抗冲击结构研究

针对在轨服务航天器末端执行器在抓捕目标时由于速度不匹配而产生的振动问题,本文受自然界中高速奔跑的袋鼠腿部结构启发,将嵌套仿生X形结构安装在服务航天器机械臂和抓捕机构之间,来抑制由碰撞所产生的抓捕后振动。通过对此系统在受到外冲击力下进行建模分析,并与传统的弹簧质量阻尼器和单层仿生X形结构的隔振性能进行对比,仿真结果表明,此嵌套仿生X形结构隔振系统具有更好的振动隔离效果,能够更加有效地抑制航天器抓捕后振动。     

基于神经网络和证据理论的火箭发动机故障诊断

针对运载火箭动力系统故障的复杂特性和故障模式的不确定性,提出了基于神经网络和证据理论的火箭发动机故障诊断。首先以火箭视加速度和角速度作为网络输入,故障类型矩阵作为网络输出,通过BP神经网络和RBF(径向基函数)神经网络进行故障诊断;之后通过D-S证据理论融合神经网络结果;最后通过滚动时域估计方法对火箭飞行状态特征量估计。仿真结果表明诊断准确率达到99%以上,表明提出的方法对于火箭发动机故障诊断具有较高的准确性和实用性。     

弹道导弹故障弹道计算方法研究

从靶场安全控制对故障弹道的需求出发,给出了弹道简化计算方法,阐述了通过改变发动机动力曲线、飞行程序角参数方式模拟导弹故障模式,并讨论了故障弹道的计算方法,仿真计算的结果满足靶场安全控制演练要求。     

航天器的故障建模与应用

针对航天器工程中的典型故障进行分析与建模.首先给出几种典型故障作用模式的具体数学形式,然后分别采用状态描述矩阵对执行机构和传感器故障进行刻画,其不同的取值对应上述不同的故障模式.另外,使用有界范数和凸面体两种形式对系统故障数学建模.通过对航天器工程中的部件故障建模可为故障诊断和容错设计提供必要的数学描述.最后通过实例简...     

国际空间站上使用的两种计算机

欧空局为国际空间站研制了两个新型的计算机 :容错计算机 ( FTC)和标准有效载荷计算机 ( SPLC) ,它们可以满足空间站对计算机的需要 ,可以方便地构成各分种布式数据管理结构。文中介绍这两种不同类型的计算机设计     

基于模糊观测器的不确定非线性系统故障检测方法

给出了一种不确定非线性系统的基于模糊观测器的故障诊断方法。用T-S模型描述不确定非线性系统,从而得到一种新的模糊观测器,并给出了其稳定性约束条件LMI。将模糊观测器和实际系统的输出之差作为残差可对不确定非线性系统进行故障检测。最后给出了一个控制系统感应电机的故障诊断应用实例。     

基于故障因子函数的鲁棒联邦滤波算法研究

针对联邦滤波器子系统传感器故障，提出了一种基于状态递推器的滑动残差检验法，并在故障检测函数的基础上，定义了故障因子函数，用于联邦滤波器的信息分配。根据新的算法，联邦滤波器的信息分配系数可根据故障因子函数进行自动调整，以减少故障通过全局重置对其他无故障子系统的污染程度，从而减少了故障信息的影响程度和持续时间，提高了故障隔离后联邦滤波器的快速恢复能力。仿真结果表明该算法是有效的。